Simultaneous object estimation and image reconstruction in a Bayesian setting

Kenneth M. Hanson
Los Alamos National Laboratory

Abstract

Suppose that it is desired to estimate certain parameters associated with a model of an object that is contained within a larger scene and that only indirect measurements of the scene are available. The optimal solution is provided by a Bayesian approach, which is founded on the posterior probability density distribution. The complete Bayesian procedure requires an integration of the posterior probability over all possible values of the image exterior to the local region being analyzed. In the presented work, the full treatment is approximated by simultaneously estimating the reconstruction outside the local region and the parameters of the model within the local region that maximize the posterior probability. A Monte Carlo procedure is employed to evaluate the usefulness of the technique in a signal-known-exactly detection task in a noisy four-view tomographic reconstruction situation.